Journal article

A comparison of multiple imputation strategies for handling missing data in multi-item scales: Guidance for longitudinal studies

Rheanna Mainzer, Jemishabye Apajee, Cattram D Nguyen, John B Carlin, Katherine J Lee



Medical research often involves using multi-item scales to assess individual characteristics, disease severity, and other health-related outcomes. It is common to observe missing data in the scale scores, due to missing data in one or more items that make up that score. Multiple imputation (MI) is a popular method for handling missing data. However, it is not clear how best to use MI in the context of scale scores, particularly when they are assessed at multiple waves of data collection resulting in large numbers of items. The aim of this article is to provide practical advice on how to impute missing values in a repeatedly measured multi-item scale using MI when inference on the scale score..

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